Optimizing Neuro-Fuzzy Approach of Internal Fault Recognition in Power Transformers by Genetic Algorithm

Transformer is one of the important devices in power systems and failure occurrence in transformer can lead to the long service interruptions as well as costly repairs. According to their importance for electric energy transferring, awareness of insulating oil condition is highly important for transformers. Studying the dissolved gases in oil can be an effective and beneficial approach to diagnose internal failures of transformers prior to irreparable failure occurrences. The results of studies about dissolved gases in oil are interpreted by experienced technicians, former experiences, and standard approaches. In this article, neural-fuzzy system is used to diagnose internal failure of transformer and dissolved gases in oil ratio. Finally, Genetic optimizing algorithm is used to improve the results.

کلیدواژه ها:

Transformer ، Dissolved gas analysis (DGA) ، Fault detecting ، ANFIS ، Genetic algorithm